What are the responsibilities and job description for the Solution Architect - Agentic AI position at Tephra?
Job Details
Description:
Role: Agentic AI Architect
Location options: Phoenix, AZ
Preface
The Agentic AI Architect is a role within TCS's AI & Data business unit in the Americas, focused on designing next-generation AI solutions that leverage autonomous "agentic" AI systems. These systems autonomously make decisions, take actions, adapt to changing environments, and continuously learn. TCS anticipates a shift from traditional chatbots to multi-agent AI frameworks where multiple agents collaborate to determine actions. This client-facing consulting position involves shaping AI architecture across various industries, delivering vertical-specific solutions for domains like BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role involves thought leadership in emerging Business Units, ensuring TCS's AI solutions are innovative, scalable, and responsibly engineered.
What You Would Be Doing
Lead AI Architecture Design: Define end-to-end architecture for AI systems incorporating autonomous agents and LLM-based components, ensuring alignment with business goals.
Client Workshops & Strategy: Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints.
Multi-Agent Framework Orchestration: Design frameworks for multi-agent systems, defining roles and ensuring robust communication and fail-safes.
Integration & Scalability: Outline integration with existing enterprise ecosystems, ensuring scalability and resilience.
Leverage Prompt Engineering & RAG: Incorporate advanced prompt engineering techniques and retrieval-augmented generation (RAG) into solution design.
Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, troubleshooting high-level architectural issues.
Industry-Tailored Solutions: Customize architectural decisions to industry-specific requirements, balancing reusability with necessary adaptations.
Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into architecture standards.
Client Engagement & Travel: Work closely with client technology leaders, presenting architectural proposals and reviewing technical designs, with travel as required.
Ethical & Safe Design: Ensure ethical AI and safety considerations are embedded from the architecture stage, documenting and mitigating potential risks.
What Skills Are Expected
AI/ML Solution Architecture: Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context.
Deep Technical Knowledge: Strong understanding of machine learning and AI techniques, especially Generative AI and large language models.
Multi-Agent System Design: Knowledge of multi-agent system patterns and frameworks.
Prompt Engineering & RAG: Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods.
AI Ethics & Responsible AI: Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations.
Cloud & Distributed Systems: Deep understanding of cloud architecture and distributed system design.
Data Management: Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.
Leadership & Communication: Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers.
Consulting and Domain Acumen: Prior consulting or client-facing experience, adept at requirement gathering and crafting proposals.
Problem-Solving & Innovation: Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills.
Continuous Learning: Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation.
Key Technology Capabilities
AI & ML Frameworks: Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.
SaaS AI & Data Platforms: Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails
Agentic AI Tooling: Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft's Semantic Kernel.
Retrieval Systems: Strong knowledge of search and retrieval technologies, including vector databases and semantic search.
Cloud Services: Expertise in cloud ecosystems (AWS, Azure, Google Cloud Platform), including cloud AI services, serverless computing, containerization, and related DevOps tools.
Programming & Scripting: Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language.
Data Platforms: Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.
Integration & APIs: Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers.
DevOps & MLOps: Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.
Security & Compliance Tools: Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.
Collaboration & Design: Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.
Emerging Tech: Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.
#LI-AD1
Role: Agentic AI Architect
Location options: Phoenix, AZ
Preface
The Agentic AI Architect is a role within TCS's AI & Data business unit in the Americas, focused on designing next-generation AI solutions that leverage autonomous "agentic" AI systems. These systems autonomously make decisions, take actions, adapt to changing environments, and continuously learn. TCS anticipates a shift from traditional chatbots to multi-agent AI frameworks where multiple agents collaborate to determine actions. This client-facing consulting position involves shaping AI architecture across various industries, delivering vertical-specific solutions for domains like BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role involves thought leadership in emerging Business Units, ensuring TCS's AI solutions are innovative, scalable, and responsibly engineered.
What You Would Be Doing
Lead AI Architecture Design: Define end-to-end architecture for AI systems incorporating autonomous agents and LLM-based components, ensuring alignment with business goals.
Client Workshops & Strategy: Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints.
Multi-Agent Framework Orchestration: Design frameworks for multi-agent systems, defining roles and ensuring robust communication and fail-safes.
Integration & Scalability: Outline integration with existing enterprise ecosystems, ensuring scalability and resilience.
Leverage Prompt Engineering & RAG: Incorporate advanced prompt engineering techniques and retrieval-augmented generation (RAG) into solution design.
Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, troubleshooting high-level architectural issues.
Industry-Tailored Solutions: Customize architectural decisions to industry-specific requirements, balancing reusability with necessary adaptations.
Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into architecture standards.
Client Engagement & Travel: Work closely with client technology leaders, presenting architectural proposals and reviewing technical designs, with travel as required.
Ethical & Safe Design: Ensure ethical AI and safety considerations are embedded from the architecture stage, documenting and mitigating potential risks.
What Skills Are Expected
AI/ML Solution Architecture: Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context.
Deep Technical Knowledge: Strong understanding of machine learning and AI techniques, especially Generative AI and large language models.
Multi-Agent System Design: Knowledge of multi-agent system patterns and frameworks.
Prompt Engineering & RAG: Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods.
AI Ethics & Responsible AI: Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations.
Cloud & Distributed Systems: Deep understanding of cloud architecture and distributed system design.
Data Management: Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.
Leadership & Communication: Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers.
Consulting and Domain Acumen: Prior consulting or client-facing experience, adept at requirement gathering and crafting proposals.
Problem-Solving & Innovation: Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills.
Continuous Learning: Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation.
Key Technology Capabilities
AI & ML Frameworks: Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.
SaaS AI & Data Platforms: Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails
Agentic AI Tooling: Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft's Semantic Kernel.
Retrieval Systems: Strong knowledge of search and retrieval technologies, including vector databases and semantic search.
Cloud Services: Expertise in cloud ecosystems (AWS, Azure, Google Cloud Platform), including cloud AI services, serverless computing, containerization, and related DevOps tools.
Programming & Scripting: Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language.
Data Platforms: Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.
Integration & APIs: Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers.
DevOps & MLOps: Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.
Security & Compliance Tools: Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.
Collaboration & Design: Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.
Emerging Tech: Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.
#LI-AD1
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